Spec
to Production.
A Linear or Jira ticket triggers an agent sandbox - code, test, iterate, open a PR. Assign an issue, get back a PR that's tested, working, and ready to merge. Cursor, Codex, and Claude Code write code, but they can't spin up a database, seed test data, or run your E2E suite - so they produce PRs that look right and break in staging. Qovery gives every AI agent a full clone of your production stack - app, database, services, seed data - so agents ship tested features, not just code diffs. On your Kubernetes. In 30 seconds.
Your AI agents
can edit files.
They can't test features.
Today's AI coding agents run in a code-only workspace. They can read files, write code, and run unit tests. But they have no database to query, no backend to call, no services to test against. The PR passes lint. It fails in staging. Every time.
The agent writes a migration. Nobody knows if it runs.
Cursor generates an ALTER TABLE migration that looks syntactically correct. But there's no PostgreSQL to run it against. The migration references a column that doesn't exist. The PR gets approved because the diff looks clean. It breaks the deploy.
The agent updates an API. Nobody knows if clients break.
Codex changes a response schema, but there's no running frontend to test against. The agent can't verify that the React app still renders correctly with the new payload. A human catches it three days later in staging.
The agent runs on someone else's cloud.
Your codebase, your database schemas, your API keys - all running on Cursor's or OpenAI's infrastructure. No audit trail. No network controls. No compliance story. For regulated industries, this is a non-starter.
From issue
to tested PR.
Ready to see this in action?
Complete features.
Not just code diffs.
Six capabilities that close the gap between 'the agent wrote code' and 'the code works in production.'
Full-stack sandbox in 30 seconds
Every issue gets a complete clone of your production stack. App containers, PostgreSQL, Redis, message queues - everything. Your platform team defines the blueprint once. Every agent gets an identical copy.
Real databases, real data
Agents query, write migrations, and validate against a live PostgreSQL, MySQL, or MongoDB. Seed data is cloned from the blueprint. The migration runs. The query returns results. No mocks.
E2E tests against the real stack
Agents run Cypress, Playwright, pytest, Jest - against running services. Integration tests hit real APIs. End-to-end tests interact with the full application. Broken code never reaches a human reviewer.
10 issues, 10 sandboxes, in parallel
Every issue gets its own isolated environment. Agents work simultaneously across your backlog. Configurable concurrency caps. No shared state, no conflicts, no queuing.
Agent governance proxy
Every outbound request from every agent is intercepted. DLP filters block API key leaks. Domain allowlists prevent data exfiltration. Kill switch stops any agent instantly. Human-in-the-loop approval for destructive operations. Full audit trail.
Your infrastructure, your rules
Agents run on your Kubernetes clusters, in your cloud account, in your region. Code and data never leave your infrastructure. Works with Claude Code, Codex, OpenCode, Cursor, or any agent. You choose the brain. Qovery provides the body.
From code-only agents
to full-stack agents.
How teams go from agents that write code to agents that ship tested features.
Connect your issue tracker
Link Linear or Jira to Qovery. Define a blueprint with your app, database, and services. Label one issue. Watch the agent spin up a sandbox, write the code, run your tests, and open a PR.
First autonomous features
Agents handle bug fixes and small features end-to-end. PRs arrive with passing E2E tests. Engineers review tested code instead of untested diffs.
Governance and scale
Multiple agents work in parallel. Platform team configures governance policies - which domains agents can reach, which operations need approval, cost caps per team.
Autonomous feature factory
Routine features flow from your backlog to merged PRs. Engineers focus on architecture and complex work. Throughput multiplied. Every agent action audited.
“The hard part was never getting an agent to write code - it was giving it a real environment to prove the code works. On Qovery, Claude Code runs unattended for hours inside an isolated, full-stack sandbox, exercises the database and services for real, and only surfaces a PR once it actually passes. We review tested features now, not hopeful diffs.”
What are AI coding agents?
How do you deploy AI agents to a real environment?
Is it safe to let AI agents deploy code?
Which AI coding agents does Qovery support?
What is the difference between an AI coding agent and an AI coding assistant?
Your agents write code.
Give them a sandbox environment.
Full-stack sandbox environments for every AI agent. Real databases, E2E tests, your infrastructure. From issue to tested PR in minutes.